{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression on MNIST (SGD: lr=0.1)\n",
    "\n",
    "This is MLP (784-10) on MNIST. SGD algorithm (lr=0.1) with 100 epoches.\n",
    "\n",
    "\n",
    "#### Original Method\n",
    "\n",
    "    Total params: 7,850\n",
    "    Trainable params: 7,850\n",
    "    Non-trainable params: 0\n",
    "\n",
    "    \n",
    "####  LR with 7840 intrinsic dim    \n",
    "    Total params: 61,559,690\n",
    "    Trainable params: 7,840\n",
    "    Non-trainable params: 61,551,850 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 is in../results/lr_mnist_sgd/170706_020027_ae92905_master_fnn_mnist_10/diary\n",
      "50 is in../results/lr_mnist_sgd/170706_020129_ae92905_master_fnn_mnist_50/diary\n",
      "100 is in../results/lr_mnist_sgd/170706_020229_ae92905_master_fnn_mnist_100/diary\n",
      "300 is in../results/lr_mnist_sgd/170706_020330_ae92905_master_fnn_mnist_300/diary\n",
      "500 is in../results/lr_mnist_sgd/170706_020431_ae92905_master_fnn_mnist_500/diary\n",
      "1000 is in../results/lr_mnist_sgd/170706_020533_ae92905_master_fnn_mnist_1000/diary\n",
      "2000 is in../results/lr_mnist_sgd/170706_020639_ae92905_master_fnn_mnist_2000/diary\n",
      "3000 is in../results/lr_mnist_sgd/170706_020751_ae92905_master_fnn_mnist_3000/diary\n",
      "4000 is in../results/lr_mnist_sgd/170706_020909_ae92905_master_fnn_mnist_4000/diary\n",
      "5000 is in../results/lr_mnist_sgd/170706_021033_ae92905_master_fnn_mnist_5000/diary\n",
      "6000 is in../results/lr_mnist_sgd/170706_021203_ae92905_master_fnn_mnist_6000/diary\n",
      "7000 is in../results/lr_mnist_sgd/170706_021338_ae92905_master_fnn_mnist_7000/diary\n",
      "7850 is in../results/lr_mnist_sgd/170706_021521_ae92905_master_fnn_mnist_7850/diary\n",
      "['../results/lr_mnist_sgd/170706_020027_ae92905_master_fnn_mnist_10/diary', '../results/lr_mnist_sgd/170706_020129_ae92905_master_fnn_mnist_50/diary', '../results/lr_mnist_sgd/170706_020229_ae92905_master_fnn_mnist_100/diary', '../results/lr_mnist_sgd/170706_020330_ae92905_master_fnn_mnist_300/diary', '../results/lr_mnist_sgd/170706_020431_ae92905_master_fnn_mnist_500/diary', '../results/lr_mnist_sgd/170706_020533_ae92905_master_fnn_mnist_1000/diary', '../results/lr_mnist_sgd/170706_020639_ae92905_master_fnn_mnist_2000/diary', '../results/lr_mnist_sgd/170706_020751_ae92905_master_fnn_mnist_3000/diary', '../results/lr_mnist_sgd/170706_020909_ae92905_master_fnn_mnist_4000/diary', '../results/lr_mnist_sgd/170706_021033_ae92905_master_fnn_mnist_5000/diary', '../results/lr_mnist_sgd/170706_021203_ae92905_master_fnn_mnist_6000/diary', '../results/lr_mnist_sgd/170706_021338_ae92905_master_fnn_mnist_7000/diary', '../results/lr_mnist_sgd/170706_021521_ae92905_master_fnn_mnist_7850/diary']\n",
      "LR model:\n",
      "(0.278638, 0.9252, 0.235294, 0.93416, 58.2311)\n",
      "\n",
      "10 dim:\n",
      "(2.2339, 0.2068, 2.24115, 0.19902, 53.5803)\n",
      "\n",
      "50 dim:\n",
      "(1.90201, 0.3608, 1.93551, 0.35, 54.4701)\n",
      "\n",
      "100 dim:\n",
      "(1.5278, 0.4967, 1.57988, 0.47414, 55.1709)\n",
      "\n",
      "300 dim:\n",
      "(0.732904, 0.7654, 0.770974, 0.75796, 55.8349)\n",
      "\n",
      "500 dim:\n",
      "(0.512836, 0.8423, 0.546393, 0.83332, 56.1863)\n",
      "\n",
      "1000 dim:\n",
      "(0.355907, 0.8955, 0.36861, 0.89204, 59.4171)\n",
      "\n",
      "2000 dim:\n",
      "(0.296049, 0.9171, 0.289724, 0.91808, 64.8826)\n",
      "\n",
      "3000 dim:\n",
      "(0.282955, 0.9202, 0.267294, 0.92474, 72.2326)\n",
      "\n",
      "4000 dim:\n",
      "(0.281383, 0.9212, 0.25627, 0.92842, 77.6289)\n",
      "\n",
      "5000 dim:\n",
      "(0.278841, 0.9224, 0.250057, 0.92988, 82.7858)\n",
      "\n",
      "6000 dim:\n",
      "(0.277397, 0.9241, 0.246654, 0.93078, 88.7108)\n",
      "\n",
      "7000 dim:\n",
      "(0.27751, 0.9252, 0.244293, 0.9314, 95.4333)\n",
      "\n",
      "7850 dim:\n",
      "(0.276793, 0.9258, 0.242897, 0.93184, 99.563)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_dir = '../results/lr_mnist_sgd'\n",
    "\n",
    "\n",
    "class Results():\n",
    "    def __init__(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []\n",
    "        \n",
    "    def add_entry(self, train_loss, train_accuracy, valid_loss, valid_accuracy, run_time):\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.train_accuracy.append(train_accuracy)\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.valid_loss.append(valid_loss)\n",
    "        self.run_time.append(run_time)\n",
    "      \n",
    "    def add_entry_list(self, entry):\n",
    "        self.add_entry(entry[0], entry[1], entry[2], entry[3], entry[4])\n",
    "        \n",
    "    def list2np(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []        \n",
    "\n",
    "dim = [10, 50, 100, 300, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 7850]\n",
    "i = 0        \n",
    "\n",
    "# filename list of diary\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "\n",
    "            \n",
    "diary_names_ordered = []\n",
    "for d in dim:\n",
    "    for f in diary_names:\n",
    "        if ('_'+str(d)+'/' in f) and ('fnn_mnist_200_200' not in f):\n",
    "            print \"%d is in\" % d + f\n",
    "            diary_names_ordered.append(f)\n",
    "        if '_dir/' in f:\n",
    "            diary_names_dir = f\n",
    "         \n",
    "print diary_names_ordered    \n",
    "        \n",
    "# extrinsic update  method\n",
    "with open(diary_names_dir,'r') as ff:\n",
    "    lines0 = ff.readlines()\n",
    "    R_dir = extract_num(lines0)\n",
    "\n",
    "print \"LR model:\\n\" + str(R_dir) + \"\\n\"\n",
    "\n",
    "# intrinsic update method\n",
    "Rs = []\n",
    "i = 0\n",
    "for fname in diary_names_ordered:\n",
    "    with open(fname,'r') as ff:\n",
    "        lines0 = ff.readlines()\n",
    "        R = extract_num(lines0)\n",
    "        print \"%d dim:\\n\"%dim[i] + str(R) + \"\\n\"\n",
    "        i += 1\n",
    "\n",
    "        Rs.append(R)\n",
    "                            \n",
    "Rs = np.array(Rs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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riHQRkUhgLPDucWXexrlKR0Ra4zTHb3UxpgZX+lhbnDfZ7qkbY4xxlWtJXVVL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3e7bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = Rs.shape[0]\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,0],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[0]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,2],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[2]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "\n",
    "ax.scatter(dim, Rs[:,0])\n",
    "ax.scatter(dim, Rs[:,2])\n",
    "\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss')\n",
    "ax.set_title('Cross Entropy  Loss')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([0.1,0.8])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJDMnk6y5d5C5ZSGZ7pInao96TjjGiNAIosKi8qbI0Mgj82GRVAmvQq3oWkev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\nm3OxNzMza3P/D+H6u5wuByOvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3e0a150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,1],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[1]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,3],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[3]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,1])\n",
    "ax.scatter(dim, Rs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Accuracy')\n",
    "ax.set_title('Cross Entropy  Accuracy')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([0.75,1.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aDqFWbC23o4n4nLe/g69+qLk7dgI1fJBHROSUZedl896y9xg2bxi7Duyi83md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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3f77110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,4],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[4]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Per Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.23390000e-01   2.06800000e-02   2.24115000e-01   1.99020000e-02\n",
      "    5.35803000e+00]\n",
      " [  3.80402000e-02   7.21600000e-03   3.87102000e-02   7.00000000e-03\n",
      "    1.08940200e+00]\n",
      " [  1.52780000e-02   4.96700000e-03   1.57988000e-02   4.74140000e-03\n",
      "    5.51709000e-01]\n",
      " [  2.44301333e-03   2.55133333e-03   2.56991333e-03   2.52653333e-03\n",
      "    1.86116333e-01]\n",
      " [  1.02567200e-03   1.68460000e-03   1.09278600e-03   1.66664000e-03\n",
      "    1.12372600e-01]\n",
      " [  3.55907000e-04   8.95500000e-04   3.68610000e-04   8.92040000e-04\n",
      "    5.94171000e-02]\n",
      " [  1.48024500e-04   4.58550000e-04   1.44862000e-04   4.59040000e-04\n",
      "    3.24413000e-02]\n",
      " [  9.43183333e-05   3.06733333e-04   8.90980000e-05   3.08246667e-04\n",
      "    2.40775333e-02]\n",
      " [  7.03457500e-05   2.30300000e-04   6.40675000e-05   2.32105000e-04\n",
      "    1.94072250e-02]\n",
      " [  5.57682000e-05   1.84480000e-04   5.00114000e-05   1.85976000e-04\n",
      "    1.65571600e-02]\n",
      " [  4.62328333e-05   1.54016667e-04   4.11090000e-05   1.55130000e-04\n",
      "    1.47851333e-02]\n",
      " [  3.96442857e-05   1.32171429e-04   3.48990000e-05   1.33057143e-04\n",
      "    1.36333286e-02]\n",
      " [  3.52602548e-05   1.17936306e-04   3.09422930e-05   1.18705732e-04\n",
      "    1.26831847e-02]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec6174bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8Fa1tgJ7P2ZuZWY5k+bL4zyQ9CWwCPiVpHLC5vGFVJnfjm5lZHnXbso+IOcDb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\ncaUsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3cb2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NRs = Rs/np.array(dim).reshape(N,1)\n",
    "print NRs\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,0],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,0])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,2],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,2])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rOioC1M58W8tl7a55tdSuWc/djy3ng5fdz13zaksdkpmZWac6KgKinfm2lsvW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\nzMzK1P8Hs4FynDyRKgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3bc5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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22j4QuB44FFgLnB4RyySNAG4H3gH8MiLOy9vnUOCXQCUwD/j3iAhJewK3AuOB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec6103b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,1],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,1])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,3],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b2DbgdnZk5tFqG3XvaxER6Xdqg3dj/fbdHGwXsGngbLYOvImt3MTA9uPY0TSF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\nmmm5igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ec3f88350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,4],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
